It is useful to know the location of people or objects for several reasons. The location in-and-of itself is important because it allows another party to find something that is lost, such as a child or a piece of expensive equipment. Location can also be valuable as a piece of data used in conjunction with other information. For example, knowledge about the location of a portable laptop computer combined with knowledge about the location of all the printers in a building allows a system to automatically route a print job from the laptop to the nearest printer, thus saving time and aggravation. The knowledge of who is in a particular room allows a system to adjust the temperature or lighting of that room to the individual's preferences or route that person's telephone calls to the phone in that room. These applications are presented here as examples illustrating the utility of a system that allows the location of a person or object to be known.
Existing systems are generally based on one of two principles. In the first method they measure the amount of time it takes a signal to travel from point A to point B then calculate the distance between the two points. Given three different distance calculations, a precise location can be determined. These types of systems typically require very precise timing. For example, since d=rt and r=3×108 meters/second, in order to locate something to within 1 meter, there is a requirement for 3.3×10−9 (3 nanoseconds) of temporal accuracy for each distance calculation. This means that the timing between the remote transmitter to be located and each of three receivers, as well as the timing between all of the receivers, must be known within 3 nSec. Various techniques have been used with varying degrees of success to overcome this timing requirement but all require fairly complex systems. Some use a centralized time base and remote receiving antennas that must be connected with special coaxial cables. Others use calibration transmitters whose location is precisely known to help compensate for timing jitter. All of these solutions require complex, expensive infrastructure.
In the second technique, existing systems attempt to calculate the distance between a transmitter and receiver based on a received signal strength indication (RSSI). While this is conceptually simpler then estimating location based on time-of-arrival (TOA) it is plagued by the issue of multipath, especially in indoor spaces. The RSSI is a function of distance and a path-loss factor: RSSI=1/d−f, where d is distance and f is the factor. However, the same radio wave travels over many paths between the transmitter and receiver. Some times these multiple waves arrive at the receiver in-phase (constructively) and some times they arrive out-of-phase (destructively). This means that the RSSI can be 3 dB higher than the actual value or as much as 30 dB lower than the true value.
This multipath fading makes it extremely difficult to determine the RSSI value accurately. The second challenge is determining the path-loss factor, f. Various techniques have been suggested to both compensate for multipath as well as the calculation of f. These typically involve calibration signals, averages of RSSI over time or diversity receiver systems. The trade-off is that these techniques, while typically simpler than the TOA calibration, lead to accuracies that are not as good as TOA based systems. It should also be noted here that “simple” is a relative term and even RSSI based systems must be quite complex in order to have acceptable accuracy.
In summary, existing solutions attempt to accurately locate the distance a transmitter is from a receiver. The accuracy of the determination of the location of a transmitter is driven by how accurately the distances can be calculated (d3, d2 and d3) between the transmitter and the various receivers (R1, R2 and R3).